It is known that in an entropy encoder/decoder, e.g., arithmetic or adaptive Huffman, a probability estimate is required of the current symbol to be encoded/decoded. The probability estimate is usually conditional in the sense that it is dependent on values of the signal at prior symbol times. The particular choice of the positions of prior symbols to be used in predicting the current symbol is called the "context configuration" in entropy encoding/decoding.
In general, a large context configuration referencing many prior symbols will provide a more accurate prediction of the current symbol. A problem with using such a large context configuration is that it is complex to implement since it requires a large memory. Additionally, it will cause a probability estimator to take a significantly longer time to reach "good" estimates of probability. Indeed, during intervals of changing signal statistics the probability estimator may be unable to track the changes with a consequent reduction in encoding/decoding efficiency.
Prior known entropy encoders/decoders have heretofore employed fixed context configurations. A problem with such arrangements is that if a particular symbol positioned prior to the current symbol by a particular number of symbol intervals, i.e., a particular "lag" interval, happened to be an excellent predictor of the current symbol but its lag interval was not in the fixed context configuration, its predictive value would not be exploited. Consequently, this limited the efficiency of the encoders/decoders.